Based on paleogeomorphology, drilling and seismic data, this paper systematically studies the structural and sedimentary evolution, source rock characteristics, reservoir characteristics and formation mechanism, hydro...Based on paleogeomorphology, drilling and seismic data, this paper systematically studies the structural and sedimentary evolution, source rock characteristics, reservoir characteristics and formation mechanism, hydrocarbon accumulation model and enrichment law in the Linhe Depression of the Hetao Basin, NW China. The Hetao Basin mainly experienced three stages of evolution, namely, weak extensional fault depression, strong extensional fault depression and strike-slip transformation, giving rise to four positive structural belts(Jilantai, Shabu, Nalinhu and Xinglong), which are favorable areas for oil and gas accumulation. The two main saline lacustrine source rocks, Lower Cretaceous Guyang Formation and Oligocene Linhe Formation, are characterized by high sulfur content, rich algae, early maturity, early expulsion, and wide oil generation window. The large structural transition belt in the intermountain area around the Hetao Basin controls the formation of large-scale braided river delta deposits, which are characterized by high quartz content(50%-76%), long-term shallow burial and weak compaction, low cement content, and good reservoir properties in delta front sandbody. The burial depth of the effective Paleogene reservoirs is predicted to reach 8000 m. Three hydrocarbon accumulation models, nose-uplift near sag, buried hill surrounding sag, fault nose near source rock, are constructed. The law of hydrocarbon accumulation in the Linhe Depression is finally clarified as follows: near-source around the depression is the foundation, high-quality thick reservoir is the premise, good tectonic setting and trap conditions are the key.展开更多
针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法...针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法从无人机立体影像中生成高精度稠密的大豆叶面真彩色三维点云,通过伽马增强的可见光绿叶指数提取植被信息,采用最佳结构元的局部阈值分割算法消除低矮杂草等噪声干扰,以达到结合可见光谱与三维点云实现复杂背景下大豆覆盖度提取的目的。选取不同时期、不同杂草混杂程度、不同地形起伏背景的大豆种植区无人机可见光影像进行试验。结果表明,该方法适用于复杂背景下的花芽分化期大豆覆盖度提取,伽马增强的绿叶指数可提高植被提取精度,结合三维点云信息的覆盖度提取总体精度达到98%以上,相比支持向量机、结合Lab颜色空间变换与Kmeans分割法、双峰阈值法等常用方法效率提高至少68%,在精度和效率方面明显优于仅利用二维影像的覆盖度提取方法。研究成果对于农田精细化管理和产量估测等具有重要的参考价值。展开更多
基金Supported by the PetroChina Key Science and Technology (2021DJ0703)。
文摘Based on paleogeomorphology, drilling and seismic data, this paper systematically studies the structural and sedimentary evolution, source rock characteristics, reservoir characteristics and formation mechanism, hydrocarbon accumulation model and enrichment law in the Linhe Depression of the Hetao Basin, NW China. The Hetao Basin mainly experienced three stages of evolution, namely, weak extensional fault depression, strong extensional fault depression and strike-slip transformation, giving rise to four positive structural belts(Jilantai, Shabu, Nalinhu and Xinglong), which are favorable areas for oil and gas accumulation. The two main saline lacustrine source rocks, Lower Cretaceous Guyang Formation and Oligocene Linhe Formation, are characterized by high sulfur content, rich algae, early maturity, early expulsion, and wide oil generation window. The large structural transition belt in the intermountain area around the Hetao Basin controls the formation of large-scale braided river delta deposits, which are characterized by high quartz content(50%-76%), long-term shallow burial and weak compaction, low cement content, and good reservoir properties in delta front sandbody. The burial depth of the effective Paleogene reservoirs is predicted to reach 8000 m. Three hydrocarbon accumulation models, nose-uplift near sag, buried hill surrounding sag, fault nose near source rock, are constructed. The law of hydrocarbon accumulation in the Linhe Depression is finally clarified as follows: near-source around the depression is the foundation, high-quality thick reservoir is the premise, good tectonic setting and trap conditions are the key.
文摘针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法从无人机立体影像中生成高精度稠密的大豆叶面真彩色三维点云,通过伽马增强的可见光绿叶指数提取植被信息,采用最佳结构元的局部阈值分割算法消除低矮杂草等噪声干扰,以达到结合可见光谱与三维点云实现复杂背景下大豆覆盖度提取的目的。选取不同时期、不同杂草混杂程度、不同地形起伏背景的大豆种植区无人机可见光影像进行试验。结果表明,该方法适用于复杂背景下的花芽分化期大豆覆盖度提取,伽马增强的绿叶指数可提高植被提取精度,结合三维点云信息的覆盖度提取总体精度达到98%以上,相比支持向量机、结合Lab颜色空间变换与Kmeans分割法、双峰阈值法等常用方法效率提高至少68%,在精度和效率方面明显优于仅利用二维影像的覆盖度提取方法。研究成果对于农田精细化管理和产量估测等具有重要的参考价值。
